Cauda EVs tRNAs analysis using data from both runs
Libraries required for data analysis
source("src.R")Data: read counts table from both runs
tRNAs_3 <- read.table(
file = "./run3_excerpt_counts/exceRpt_tRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
tRNAs_3 <- as.data.frame(tRNAs_3)
counts_total_run3 <- tRNAs_3
tRNAs_4 <- read.table(
file = "run4_excerpt_counts_/exceRpt_tRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
tRNAs_4 <- as.data.frame(tRNAs_4)
counts_total_run3 <- tRNAs_3
counts_total_run4 <- tRNAs_4
colnames(counts_total_run3) <- c(
"MS28F1_sample11", "MS28F1_sample14",
"MS28F1_sample2", "MS28F1_sample3",
"MS28F1_sample4", "MS28F1_sample5",
"MS28F1_sample6", "MS28F1_sample7",
"MS28F1_sample8", "MS28F1_sample9"
)
colnames(counts_total_run4) <- c(
"MS28F1_sample11_2", "MS28F1_sample14_2",
"MS28F1_sample2_2", "MS28F1_sample3_2",
"MS28F1_sample4_2", "MS28F1_sample5_2",
"MS28F1_sample6_2", "MS28F1_sample7_2",
"MS28F1_sample8_2", "MS28F1_sample9_2"
)
mlist <- list(counts_total_run3, counts_total_run4)
df <- mlist[[1]]
for (i in 2:length(mlist)) {
df <- merge(df, mlist[[i]], by = "row.names", all = T)
rownames(df) <- df$Row.names
df <- df[, !(names(df) %in% "Row.names")]
knitr::kable(head(df))
}Specifying the group & batch while filterByExprs()
counts_total <- df
d <- data.frame(
row.names = colnames(counts_total),
group = c(
"G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1", "G2",
"G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
),
batch = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
)
counts_total[is.na(counts_total)] <- 0
dds <- calcNormFactors(DGEList(counts_total), method = "TMM")
dds$samples$group <- c(
"G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1",
"G2", "G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
)
dds$samples$batch <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
mm <- model.matrix(~ batch + group, data = d)
dds <- dds[which(filterByExpr(dds, design = mm, min.count = 10, min.prop = 0.8)), ]
en <- log1p(edgeR::cpm(dds)) # normalization by counts per millionDifferential Expression analysis without Surrogate Variable analysis
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_without_ruvseq, n = 20))| logFC | logCPM | LR | PValue | FDR | |
|---|---|---|---|---|---|
| Tyr | -0.4893745 | 7.935688 | 3.8666022 | 0.0492561 | 0.5421865 |
| Ile | -0.4297720 | 9.694536 | 3.0391174 | 0.0812801 | 0.5421865 |
| Cys | 0.2972731 | 12.607438 | 2.6750203 | 0.1019340 | 0.5421865 |
| Asp | -0.2428079 | 12.437206 | 2.0769683 | 0.1495369 | 0.5421865 |
| Glu | 0.2244529 | 17.474000 | 1.9452016 | 0.1631049 | 0.5421865 |
| SeC | 0.2683224 | 10.280610 | 1.8293835 | 0.1762004 | 0.5421865 |
| Pro | 0.2254161 | 9.635737 | 1.7309576 | 0.1882886 | 0.5421865 |
| Val | 0.3290303 | 14.720771 | 1.6632971 | 0.1971587 | 0.5421865 |
| Ser | 0.2423846 | 10.004283 | 1.2642794 | 0.2608426 | 0.6376153 |
| Gly | 0.1848298 | 19.674351 | 1.0426427 | 0.3072076 | 0.6758568 |
| Arg | -0.1978367 | 10.647809 | 0.8140622 | 0.3669220 | 0.7338440 |
| iMet | 0.1298134 | 11.672785 | 0.5572120 | 0.4553858 | 0.8348739 |
| Gln | 0.1184996 | 10.309014 | 0.2377104 | 0.6258647 | 0.9179376 |
| His | 0.1008914 | 12.581947 | 0.1950451 | 0.6587504 | 0.9179376 |
| Lys | -0.0569287 | 14.572036 | 0.1313012 | 0.7170867 | 0.9179376 |
| Leu | 0.0523660 | 10.795842 | 0.0894270 | 0.7649068 | 0.9179376 |
| Ala | 0.0545848 | 10.771378 | 0.0837683 | 0.7722542 | 0.9179376 |
| Asn | -0.0582296 | 9.239539 | 0.0459426 | 0.8302802 | 0.9179376 |
| Thr | -0.0424330 | 9.346387 | 0.0205495 | 0.8860129 | 0.9179376 |
| Phe | 0.0314313 | 6.912327 | 0.0183823 | 0.8921522 | 0.9179376 |
PCA plots without Surrogate Variable analysis
All samples (normalized by counts-per-million), colored by treatment group
plgINS::plPCA(en,
colorBy = dds$samples$group,
add.labels = FALSE, points.size = 15
)All samples (normalized by counts-per-million), colored by library size
Note: Even after the normalization, the libraries are separated based on lib.size, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary ###
plgINS::plPCA(en,
colorBy = dds$samples$lib.size,
add.labels = FALSE, points.size = 15
)All samples (normalized by counts-per-million), colored by sequencing batch
Note: Even after the normalization, the libraries are separated based on the sequencing batch, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary
plgINS::plPCA(en,
colorBy = dds$samples$batch,
add.labels = FALSE, points.size = 15
)Differential Expression analysis with Surrogate Variable analysi, SV1
re <- RUVSeq::RUVs(en,
cIdx = row.names(en), k = 1,
scIdx = RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1] # add the variable (suppose I have only one) to the colData dataframe
mm <- model.matrix(~ SV1 + batch + group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_with_ruvseq1, n = 20))| logFC | logCPM | LR | PValue | FDR | |
|---|---|---|---|---|---|
| Cys | 0.3149355 | 12.610101 | 3.6056944 | 0.0575820 | 0.3416520 |
| Ile | -0.2592603 | 9.666343 | 3.5504357 | 0.0595298 | 0.3416520 |
| SeC | 0.3189241 | 10.290391 | 3.5327978 | 0.0601662 | 0.3416520 |
| Tyr | -0.3235718 | 7.874531 | 3.4798988 | 0.0621185 | 0.3416520 |
| Pro | 0.2385796 | 9.632674 | 2.5146838 | 0.1127903 | 0.4763612 |
| Glu | 0.2060507 | 17.474065 | 2.0768625 | 0.1495473 | 0.4763612 |
| Asp | -0.1918334 | 12.442074 | 2.0563839 | 0.1515695 | 0.4763612 |
| Ser | 0.2383967 | 9.973714 | 1.3190278 | 0.2507666 | 0.6197481 |
| Ala | 0.1386194 | 10.757208 | 1.2480900 | 0.2639176 | 0.6197481 |
| Val | 0.2605593 | 14.721542 | 1.1445959 | 0.2846833 | 0.6197481 |
| Gly | 0.1677909 | 19.674375 | 1.0240802 | 0.3115531 | 0.6197481 |
| iMet | 0.1492808 | 11.662440 | 0.9178337 | 0.3380444 | 0.6197481 |
| Asn | 0.1740746 | 9.227860 | 0.7597973 | 0.3833920 | 0.6365502 |
| Trp | 0.1403309 | 9.786448 | 0.6458976 | 0.4215832 | 0.6365502 |
| Thr | 0.1810413 | 9.332521 | 0.6120646 | 0.4340115 | 0.6365502 |
| His | 0.1343053 | 12.584365 | 0.3713364 | 0.5422767 | 0.7216184 |
| Leu | 0.0895625 | 10.782906 | 0.3438519 | 0.5576142 | 0.7216184 |
| Gln | 0.1270955 | 10.295692 | 0.2756150 | 0.5995898 | 0.7245608 |
| Arg | -0.0585177 | 10.635208 | 0.2378586 | 0.6257571 | 0.7245608 |
| Phe | 0.0833018 | 6.792185 | 0.1871275 | 0.6653182 | 0.7318501 |
PCA plots with Surrogate variable analysis, SV=1
All samples, colored by treatment group
plgINS::plPCA(re$normalizedCounts,
colorBy = d$group,
add.labels = FALSE, points.size = 15
)All samples, colored by library size
Note: We can observe that the samples are clustering more by groups & spread by counts. Libraries with high/low counts are not clustered together anymore. No effect of library size, keeping the group effect:
plgINS::plPCA(re$normalizedCounts,
colorBy = dds$samples$lib.size,
add.labels = FALSE, points.size = 15
)All samples, colored by sequencing batch
Note: We can observe that the samples are corrected for sequencing batch effect:
plgINS::plPCA(re$normalizedCounts,
colorBy = dds$samples$batch,
add.labels = FALSE, points.size = 15
)References
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SessionInfo
devtools::session_info() %>%
details::details()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate de_DE.UTF-8
ctype de_DE.UTF-8
tz Europe/Zurich
date 2021-06-28
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date lib source
annotate 1.68.0 2020-10-27 [1] Bioconductor
AnnotationDbi * 1.52.0 2020-10-27 [1] Bioconductor
AnnotationFilter 1.14.0 2020-10-27 [1] Bioconductor
AnnotationHub 2.22.1 2021-04-16 [1] Bioconductor
aroma.light 3.20.0 2020-10-27 [1] Bioconductor
ash 1.0-15 2015-09-01 [1] CRAN (R 4.0.4)
askpass 1.1 2019-01-13 [1] CRAN (R 4.0.4)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.4)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.4)
bayestestR 0.9.0 2021-04-08 [1] CRAN (R 4.0.4)
beeswarm 0.3.1 2021-03-07 [1] CRAN (R 4.0.4)
Biobase * 2.50.0 2020-10-27 [1] Bioconductor
BiocFileCache 1.14.0 2020-10-27 [1] Bioconductor
BiocGenerics * 0.36.1 2021-04-16 [1] Bioconductor
BiocManager 1.30.12 2021-03-28 [1] CRAN (R 4.0.4)
BiocParallel * 1.24.1 2020-11-06 [1] Bioconductor
BiocVersion 3.12.0 2020-04-27 [1] Bioconductor
biomaRt * 2.46.3 2021-02-09 [1] Bioconductor
Biostrings * 2.58.0 2020-10-27 [1] Bioconductor
bit 4.0.4 2020-08-04 [1] CRAN (R 4.0.4)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.0.4)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.0.4)
blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.4)
bookdown 0.22 2021-04-22 [1] CRAN (R 4.0.4)
boot 1.3-27 2021-02-12 [1] CRAN (R 4.0.4)
broom 0.7.7 2021-06-13 [1] CRAN (R 4.0.4)
bslib 0.2.4 2021-01-25 [1] CRAN (R 4.0.4)
cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.4)
callr 3.7.0 2021-04-20 [1] CRAN (R 4.0.4)
caTools 1.18.2 2021-03-28 [1] CRAN (R 4.0.4)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.4)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.4)
clipr 0.7.1 2020-10-08 [1] CRAN (R 4.0.4)
coda 0.19-4 2020-09-30 [1] CRAN (R 4.0.4)
codetools 0.2-18 2020-11-04 [1] CRAN (R 4.0.4)
colorRamps 2.3 2012-10-29 [1] CRAN (R 4.0.4)
colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.4)
conquer 1.0.2 2020-08-27 [1] CRAN (R 4.0.4)
cqn * 1.36.0 2020-10-27 [1] Bioconductor
crayon 1.4.1 2021-02-08 [1] CRAN (R 4.0.4)
crosstalk 1.1.1 2021-01-12 [1] CRAN (R 4.0.4)
curl 4.3 2019-12-02 [1] CRAN (R 4.0.4)
data.table 1.14.0 2021-02-21 [1] CRAN (R 4.0.4)
DBI 1.1.1 2021-01-15 [1] CRAN (R 4.0.4)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.0.4)
DelayedArray 0.16.3 2021-03-24 [1] Bioconductor
desc 1.3.0 2021-03-05 [1] CRAN (R 4.0.4)
DESeq2 * 1.30.1 2021-02-19 [1] Bioconductor
details 0.2.1 2020-01-12 [1] CRAN (R 4.0.4)
devtools 2.4.2 2021-06-07 [1] CRAN (R 4.0.4)
digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.4)
doParallel 1.0.16 2020-10-16 [1] CRAN (R 4.0.4)
dplyr * 1.0.5 2021-03-05 [1] CRAN (R 4.0.4)
EDASeq * 2.24.0 2020-10-27 [1] Bioconductor
edgeR * 3.32.1 2021-01-14 [1] Bioconductor
effectsize 0.4.4-1 2021-04-05 [1] CRAN (R 4.0.4)
ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.4)
emmeans 1.6.0 2021-04-24 [1] CRAN (R 4.0.4)
EnhancedVolcano * 1.8.0 2020-10-27 [1] Bioconductor
ensembldb 2.14.1 2021-04-19 [1] Bioconductor
estimability 1.3 2018-02-11 [1] CRAN (R 4.0.4)
evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.4)
extrafont 0.17 2014-12-08 [1] CRAN (R 4.0.4)
extrafontdb 1.0 2012-06-11 [1] CRAN (R 4.0.4)
fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.4)
farver 2.1.0 2021-02-28 [1] CRAN (R 4.0.4)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.4)
foreach 1.5.1 2020-10-15 [1] CRAN (R 4.0.4)
fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.4)
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P ── Loaded and on-disk path mismatch.